Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future

Kumar, Senthil; Fred, A. Lenin; Gulyás, Balázs; Kumar, Himanshu; Miriam, L. R. Jonisha

doi:10.1007/978-981-15-6321-8_3

Cited by 9 publications

(3 citation statements)

References 88 publications

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“…Despite deep learning and CNN's dominance in visual recognition tasks [7][8][9], several new text recognition methods have been proposed, with significant improvements. Ghosh et al [10] introduced a visual attention model using the Long-Short Term Memory (LSTM) network composed under the encoder-decoder.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

Prabu¹,

Sundar²

2023

Intelligent Automation &Amp; Soft Computing

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Recognizing irregular text in natural images is a challenging task in computer vision. The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes. In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture. The proposed framework is divided into four main modules. Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image. Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate on text regions and obtain enriched feature maps. Thirdly, a deep bi-directional long short-term memory (Bi-LSTM) network is used to make a contextual feature map out of a visual feature map generated from a Convolutional Neural Network (CNN). Finally, we propose a Dual Step Attention Mechanism (DSAM) integrated with the Connectionist Temporal Classification (CTC) -Attention decoder to re-weights visual features and focus on the intra-sequence relationships to generate a more accurate character sequence. The effectiveness of our proposed framework is verified through extensive experiments on various benchmarks datasets, such as SVT, ICDAR, CUTE80, and IIIT5k. The performance of the proposed text recognition framework is analyzed with the accuracy metric. Demonstrate that our proposed method outperforms the existing approaches on both regular and irregular text. Additionally, the robustness of our approach is evaluated using the grocery datasets, such as GroZi-120, Web-Market, SKU-110K, and Freiburg Groceries datasets that contain complex text images. Still, our framework produces superior performance on grocery datasets.

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Section: Related Workmentioning

confidence: 99%

Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

Prabu¹,

Sundar²

2023

Intelligent Automation &Amp; Soft Computing

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“…Table 2 provides an accuracy of our model with existing works related to tuberculosis and fnds to be superior when compared to other existing works. [30,31]. Te model was trained for 100 epochs.…”

Section: Sequential Model For Tuberculosismentioning

confidence: 99%

Lung Diseases Detection Using Various Deep Learning Algorithms

Priyadarsini

Kotecha

Rajini

et al. 2023

Journal of Healthcare Engineering

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The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient’s treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.

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“…The medical images are very significant in detecting cancer in its early stage and plan the treatment accordingly. Detection of cancer from medical images using deep learning systems involve three phases, namely, pre-processing, segmentation and post-processing for better analysis of images to detect cancer disease [11]. Each phase in detecting cancer based on medical images involves applying different methods before training the deep-learning algorithm.…”

Section: Cancer Detection Processmentioning

confidence: 99%

Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges

Kumar¹,

Alqahtani²

2022

Computer Modeling in Engineering &Amp; Sciences

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Cancer is one of the most critical diseases that has caused several deaths in today's world. In most cases, doctors and practitioners are only able to diagnose cancer in its later stages. In the later stages, planning cancer treatment and increasing the patient's survival rate becomes a very challenging task. Therefore, it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning. Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease. However, manual interpretation of medical images is costly, time-consuming and biased. Nowadays, deep learning, a subset of artificial intelligence, is gaining increasing attention from practitioners in automatically analysing and interpreting medical images without their intervention. Deep learning methods have reported extraordinary results in different fields due to their ability to automatically extract intrinsic features from images without any dependence on manually extracted features. This study provides a comprehensive review of deep learning methods in cancer detection and diagnosis, mainly focusing on breast cancer, brain cancer, skin cancer, and prostate cancer. This study describes various deep learning models and steps for applying deep learning models in detecting cancer. Recent developments in cancer detection based on deep learning methods have been critically analysed and summarised to identify critical challenges in applying them for detecting cancer accurately in the early stages. Based on the identified challenges, we provide a few promising future research directions for fellow researchers in the field. The outcome of this study provides many clues for developing practical and accurate cancer detection systems for its early diagnosis and treatment planning.

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Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future

Cited by 9 publications

References 88 publications

Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

Lung Diseases Detection Using Various Deep Learning Algorithms

Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges

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